Abstract
The cellular neural/nonlinear network (CNN) has become a useful tool for image and signal processing, robotic and biological visions, and higher brain functions. Based on our previous research, this paper set up two new theorems of robust designs for Pattern Matching CNN in processing binary images, which provide parameter inequalities to determine parameter intervals for implementing the prescribed image processing function. Three numerical simulation examples are given.
This project is jointly supported by the National Natural Science Foundations of China (Grant Nos. 60674059, 70271068), the Research Fund for the Doctoral Program of Higher Education (Grant No. 20020008004) by the Ministry of Education of China.
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Zhao, B., Li, W., Jian, S., Min, L. (2007). Two Theorems on the Robust Designs for Pattern Matching CNNs. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4493. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72395-0_20
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DOI: https://doi.org/10.1007/978-3-540-72395-0_20
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